The downloaded binary packages are in
/var/folders/4t/vnn_z2415xb2dn4t7zflxlqh0000gn/T//Rtmp7qmXRk/downloaded_packages
library(plotly)## data extracted from New York Times state-level data from NYT Github repositorycv_states <-read.csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv")## state-level population information from us_census_data available on GitHub repository:state_pops <-read.csv("https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv")# adjust column namesstate_pops$abb <- state_pops$statestate_pops$state <- state_pops$state_namestate_pops$state_name <-NULL### FINISH THE CODE HEREcv_states <-merge(cv_states,state_pops, by="state")
2. Look at the data
Inspect the dimensions, head, and tail of the data
Inspect the structure of each variables
dim(cv_states)
[1] 58094 9
head(cv_states)
state date fips cases deaths geo_id population pop_density abb
1 Alabama 2023-01-04 1 1587224 21263 1 4887871 96.50939 AL
2 Alabama 2020-04-25 1 6213 213 1 4887871 96.50939 AL
3 Alabama 2023-02-26 1 1638348 21400 1 4887871 96.50939 AL
4 Alabama 2022-12-03 1 1549285 21129 1 4887871 96.50939 AL
5 Alabama 2020-05-06 1 8691 343 1 4887871 96.50939 AL
6 Alabama 2021-04-21 1 524367 10807 1 4887871 96.50939 AL
# format the datecv_states$date <-as.Date(cv_states$date, format="%Y-%m-%d")# format the state and state abbreviation (abb) variablesstate_list <-unique(cv_states$state)cv_states$state <-factor(cv_states$state, levels = state_list)abb_list <-unique(cv_states$abb)cv_states$abb <-factor(cv_states$abb, levels = abb_list)### FINISH THE CODE HERE # order the data first by state, second by datecv_states <- cv_states[order(cv_states$state,cv_states$date),]# Confirm the variables are now correctly formattedstr(cv_states)
# Inspect the range values for each variable.head(cv_states)
state date fips cases deaths geo_id population pop_density abb
1029 Alabama 2020-03-13 1 6 0 1 4887871 96.50939 AL
597 Alabama 2020-03-14 1 12 0 1 4887871 96.50939 AL
282 Alabama 2020-03-15 1 23 0 1 4887871 96.50939 AL
12 Alabama 2020-03-16 1 29 0 1 4887871 96.50939 AL
266 Alabama 2020-03-17 1 39 0 1 4887871 96.50939 AL
78 Alabama 2020-03-18 1 51 0 1 4887871 96.50939 AL
summary(cv_states)
state date fips cases
Washington : 1158 Min. :2020-01-21 Min. : 1.00 Min. : 1
Illinois : 1155 1st Qu.:2020-12-06 1st Qu.:16.00 1st Qu.: 112125
California : 1154 Median :2021-09-11 Median :29.00 Median : 418120
Arizona : 1153 Mean :2021-09-10 Mean :29.78 Mean : 947941
Massachusetts: 1147 3rd Qu.:2022-06-17 3rd Qu.:44.00 3rd Qu.: 1134318
Wisconsin : 1143 Max. :2023-03-23 Max. :72.00 Max. :12169158
(Other) :51184
deaths geo_id population pop_density
Min. : 0 Min. : 1.00 Min. : 577737 Min. : 1.292
1st Qu.: 1598 1st Qu.:16.00 1st Qu.: 1805832 1st Qu.: 43.659
Median : 5901 Median :29.00 Median : 4468402 Median : 107.860
Mean : 12553 Mean :29.78 Mean : 6397965 Mean : 423.031
3rd Qu.: 15952 3rd Qu.:44.00 3rd Qu.: 7535591 3rd Qu.: 229.511
Max. :104277 Max. :72.00 Max. :39557045 Max. :11490.120
NA's :1106
abb
WA : 1158
IL : 1155
CA : 1154
AZ : 1153
MA : 1147
WI : 1143
(Other):51184
min(cv_states$date)
[1] "2020-01-21"
max(cv_states$date)
[1] "2023-03-23"
4. Add new_cases and new_deaths and correct outliers
Add variables for new cases, new_cases, and new deaths, new_deaths:
Hint: You can set new_cases equal to the difference between cases on date i and date i-1, starting on date i=2
Filter to dates after June 1, 2021
Use plotly for EDA: See if there are outliers or values that don’t make sense for new_cases and new_deaths. Which states and which dates have strange values?
Correct outliers: Set negative values for new_cases or new_deaths to 0
Recalculate cases and deaths as cumulative sum of updated new_cases and new_deaths
Get the rolling average of new cases and new deaths to smooth over time
Inspect data again interactively
# Add variables for new_cases and new_deaths:for (i in1:length(state_list)) { cv_subset <-subset(cv_states, state == state_list[i]) cv_subset <- cv_subset[order(cv_subset$date),]# add starting level for new cases and deaths cv_subset$new_cases <- cv_subset$cases[1] cv_subset$new_deaths <- cv_subset$deaths[1]### FINISH THE CODE HEREfor (j in2:nrow(cv_subset)) { cv_subset$new_cases[j] <- cv_subset$cases[j] - cv_subset$cases[j-1] cv_subset$new_deaths[j] <- cv_subset$deaths[j] - cv_subset$deaths[j-1] }# include in main dataset cv_states$new_cases[cv_states$state==state_list[i]] <- cv_subset$new_cases cv_states$new_deaths[cv_states$state==state_list[i]] <- cv_subset$new_deaths}# Focus on recent datescv_states <- cv_states |> dplyr::filter(date >="2021-06-01")# Inspect outliers in new_cases using plotlyp1 <-ggplot(cv_states, aes(x = date, y = new_cases, color = state)) +geom_point(size = .5, alpha =0.5)ggplotly(p1)
p2 <-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) +geom_point(size = .5, alpha =0.5)ggplotly(p2)
# set negative new case or death counts to 0cv_states$new_cases[cv_states$new_cases<0] <-0cv_states$new_deaths[cv_states$new_deaths<0] <-0# Re-calculate `cases` and `deaths` as cumulative sum of updated `new_cases` and `new_deaths`for (i in1:length(state_list)) { cv_subset =subset(cv_states, state == state_list[i])# add starting level for new cases and deaths cv_subset$cases <- cv_subset$cases[1] cv_subset$deaths <- cv_subset$deaths[1]### FINISH CODE HEREfor (j in2:nrow(cv_subset)) { cv_subset$cases[j] <- cv_subset$new_cases[j] + cv_subset$cases[j-1] cv_subset$deaths[j] <- cv_subset$new_deaths[j] + cv_subset$deaths[j-1] }# include in main dataset cv_states$cases[cv_states$state==state_list[i]] <- cv_subset$cases cv_states$deaths[cv_states$state==state_list[i]] <- cv_subset$deaths}# Smooth new countscv_states$new_cases <- zoo::rollmean(cv_states$new_cases, k=7, fill=NA, align='right') |>round(digits =0)cv_states$new_deaths <- zoo::rollmean(cv_states$new_deaths, k=7, fill=NA, align='right') |>round(digits =0)# Inspect data again interactivelyp2 <-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) +geom_line() +geom_point(size = .5, alpha =0.5)ggplotly(p2)
5. Add additional variables
Add population-normalized (by 100,000) variables for each variable type (rounded to 1 decimal place). Make sure the variables you calculate are in the correct format (numeric). You can use the following variable names:
per100k = cases per 100,000 population
newper100k= new cases per 100,000
deathsper100k = deaths per 100,000
newdeathsper100k = new deaths per 100,000
Add a naive CFR variable representing deaths / cases on each date for each state
Create a data frame representing values on the most recent date, cv_states_today, as done in lecture
### FINISH CODE HERE# add population normalized (by 100,000) counts for each variablecv_states$per100k <-as.numeric(format(round(cv_states$cases/(cv_states$population/100000),1),nsmall=1))cv_states$newper100k <-as.numeric(format(round(cv_states$new_cases/(cv_states$population/100000),1),nsmall=1))cv_states$deathsper100k <-as.numeric(format(round(cv_states$deaths/(cv_states$population/100000),1),nsmall=1))cv_states$newdeathsper100k <-as.numeric(format(round(cv_states$new_deaths/(cv_states$population/100000),1),nsmall=1))# add a naive_CFR variable = deaths / casescv_states <- cv_states |>mutate(naive_CFR =round((deaths*100/cases),2))# create a `cv_states_today` variablecv_states_today <-subset(cv_states, date==max(cv_states$date))
II. Scatterplots
6. Explore scatterplots using plot_ly()
Create a scatterplot using plot_ly() representing pop_density vs. various variables (e.g. cases, per100k, deaths, deathsper100k) for each state on most recent date (cv_states_today)
Color points by state and size points by state population
Use hover to identify any outliers.
Remove those outliers and replot.
Choose one plot. For this plot:
Add hoverinfo specifying the state name, cases per 100k, and deaths per 100k, similarly to how we did this in the lecture notes
Add layout information to title the chart and the axes
Enable hovermode = "compare"
### FINISH CODE HERE# pop_density vs. casescv_states_today |>plot_ly(x =~pop_density, y =~cases, type ="scatter", mode ='markers', color =~state,size =~population, sizes =c(5, 70), marker =list(sizemode='diameter', opacity=0.5))
# filter out "District of Columbia"cv_states_today_filter <- cv_states_today |>filter(state!="District of Columbia")# pop_density vs. cases after filteringcv_states_today_filter |>plot_ly(x =~pop_density, y =~cases, type ="scatter", mode ='markers', color =~state,size =~population, sizes =c(5, 70), marker =list(sizemode='diameter', opacity=0.5))
# pop_density vs. deathsper100kcv_states_today_filter |>plot_ly(x =~pop_density, y =~deathsper100k,type ="scatter", mode ='markers', color =~state,size =~population, sizes =c(5, 70), marker =list(sizemode='diameter', opacity=0.5))
# Adding hoverinfocv_states_today_filter |>plot_ly(x =~pop_density, y =~deathsper100k,type ="scatter", mode ='markers', color =~state,size =~population, sizes =c(5, 70), marker =list(sizemode='diameter', opacity=0.5),hoverinfo ='text',text =~paste( paste(state, ":", sep=""), paste(" Cases per 100k: ", per100k, sep="") , paste(" Deaths per 100k: ", deathsper100k, sep=""), sep ="<br>")) |>layout(title ="Population-normalized COVID-19 deaths (per 100k) vs. population density for US states",yaxis =list(title ="Deaths per 100k"), xaxis =list(title ="Population Density"),hovermode ="compare")
7. Explore scatterplot trend interactively using ggplotly() and geom_smooth()
For pop_density vs. newdeathsper100k create a chart with the same variables using gglot_ly()
Explore the pattern between and using geom_smooth()
Create a line chart of the naive_CFR for all states over time using plot_ly()
Use the zoom and pan tools to inspect the naive_CFR for the states that had an increase in September.
Create one more line chart, for Florida only, which shows new_cases and new_deaths together in one plot. Hint: look for an add_*()
Use hoverinfo to “eyeball” the approximate peak of deaths and peak of cases. What is the time delay between the peak of cases and the peak of deaths?
### FINISH CODE HERE# Line chart for naive_CFR for all states over time using `plot_ly()`plot_ly(cv_states, x =~date, y =~naive_CFR, color =~state, type ="scatter", mode ="lines")
### FINISH CODE HERE# Line chart for Florida showing new_cases and new_deaths together (two lines)cv_states |>filter(state=="Florida") |>plot_ly(x =~date, y =~new_cases, type ="scatter", mode ="lines") |>add_lines(x =~date, y =~new_deaths, type ="scatter", mode ="lines")
the number of cases peaked in July 2021 and number of deaths in January 2022
9. Heatmaps
Create a heatmap to visualize new_cases for each state on each date greater than June 1st, 2021 - Start by mapping selected features in the dataframe into a matrix using the tidyr package function pivot_wider(), naming the rows and columns, as done in the lecture notes - Use plot_ly() to create a heatmap out of this matrix. Which states stand out? - Repeat with newper100k variable. Now which states stand out? - Create a second heatmap in which the pattern of new_cases for each state over time becomes more clear by filtering to only look at dates every two weeks
### FINISH CODE HERE# Map state, date, and new_cases to a matrixlibrary(tidyr)cv_states_mat <- cv_states |>select(state, date, new_cases) |> dplyr::filter(date>as.Date("2021-06-15"))cv_states_mat2 <-as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = new_cases))rownames(cv_states_mat2) <- cv_states_mat2$datecv_states_mat2$date <-NULLcv_states_mat2 <-as.matrix(cv_states_mat2)# Create a heatmap using plot_ly()plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),z=~cv_states_mat2,type="heatmap",showscale=T)
# Create a second heatmap after filtering to only include dates every other weekfilter_dates <-seq(as.Date("2021-06-15"), as.Date("2021-11-01"), by="2 weeks")cv_states_mat <- cv_states |>select(state, date, newper100k) |>filter(date %in% filter_dates)cv_states_mat2 <-as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = newper100k))rownames(cv_states_mat2) <- cv_states_mat2$datecv_states_mat2$date <-NULLcv_states_mat2 <-as.matrix(cv_states_mat2)# Create a heatmap using plot_ly()plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),z=~cv_states_mat2,type="heatmap",showscale=TRUE)
10. Map
Create a map to visualize the naive_CFR by state on October 15, 2021
Compare with a map visualizing the naive_CFR by state on most recent date
Plot the two maps together using subplot(). Make sure the shading is for the same range of values (google is your friend for this)
Describe the difference in the pattern of the CFR.
### For specified datepick.date <-"2021-10-15"# Extract the data for each state by its abbreviationcv_per100 <- cv_states |>filter(date==pick.date) |>select(state, abb, newper100k, cases, deaths) # select datacv_per100$state_name <- cv_per100$statecv_per100$state <- cv_per100$abbcv_per100$abb <-NULL# Create hover textcv_per100$hover <-with(cv_per100, paste(state_name, '<br>', "Cases per 100k: ", newper100k, '<br>', "Cases: ", cases, '<br>', "Deaths: ", deaths))# Set up mapping detailsset_map_details <-list(scope ='usa',projection =list(type ='albers usa'),showlakes =TRUE,lakecolor =toRGB('white'))# Make sure both maps are on the same color scaleshadeLimit <-125# Create the mapfig <-plot_geo(cv_per100, locationmode ='USA-states') |>add_trace(z =~newper100k, text =~hover, locations =~state,color =~newper100k, colors ='Purples' )fig <- fig |>colorbar(title =paste0("Cases per 100k: ", pick.date), limits =c(0,shadeLimit))fig <- fig |>layout(title =paste('Cases per 100k by State as of ', pick.date, '<br>(Hover for value)'),geo = set_map_details )fig_pick.date <- fig################ Map for today's date# Extract the data for each state by its abbreviationcv_per100 <- cv_states_today |>select(state, abb, newper100k, cases, deaths) # select datacv_per100$state_name <- cv_per100$statecv_per100$state <- cv_per100$abbcv_per100$abb <-NULL# Create hover textcv_per100$hover <-with(cv_per100, paste(state_name, '<br>', "Cases per 100k: ", newper100k, '<br>', "Cases: ", cases, '<br>', "Deaths: ", deaths))# Set up mapping detailsset_map_details <-list(scope ='usa',projection =list(type ='albers usa'),showlakes =TRUE,lakecolor =toRGB('white'))# Create the mapfig <-plot_geo(cv_per100, locationmode ='USA-states') |>add_trace(z =~newper100k, text =~hover, locations =~state,color =~newper100k, colors ='Purples' )fig <- fig |>colorbar(title =paste0("Cases per 100k: ", Sys.Date()), limits =c(0,shadeLimit))fig <- fig |>layout(title =paste('Cases per 100k by State as of', Sys.Date(), '<br>(Hover for value)'),geo = set_map_details )fig_Today <- fig### Plot together subplot(fig_pick.date, fig_Today, nrows =2, margin = .05)